Computing for Data Analysis

Computing for Data Analysis is an introduction to the R programming language, but not an introduction to programming. This course is designed for people who already know how to program. The course description makes it seem like the class is intended for everyone, even those who do not know how to program at all, which caused a lot of pain in the class forums. The class description estimates the effort required to complete the class at 3-5 hours per week. Although this range is probably accurate for weeks 1 and 4, week 2 and especially week 3 are likely to take significantly longer than that. I wouldn't say the course is impossible for someone who hasn't programmed before, but I imagine it would take dozens of hours to understand what's going on, because the lectures move through material at a rate and level of sophistication that assumes prior programming experience. For example, in week two the lectures cover control flow, loops, defining functions, various built in functions, scoping, higher order functions and debugging. That is a ton of ground to cover in one week of an introductory class that claims 3-5 hours of effort per week. Thus if you have no prior experience I'd avoid this course in favor of a true introduction to computer science course like Udacity 101, Rice's intro to CS on Courera, MIT 6.00, or Harvard CS50. All of these are great courses that either have sessions starting up soon or let you learn at your own pace at any time.

The lectures in this course were presented as online videos of up to about 1/2 hour length. The videos consisted of prepared slides with the lecturers voice in the soundtrack. To keep the videos from becoming too boring, there were multiple choice questions interspersed throughout the videos. There were also some activities for the viewer to try at the R command line. The production values were comparable to what I regularly see in lecture capture videos from conventional courses- these were not highly polished videos

I was disappointed by the lack of depth in the lecture material. This was a course in R programming rather than statistics, so any in-depth discussion of statistical issues would have been inappropriate, but I was surprised by how little discussion there was of the structure and semantics of the R programming language. For example, R uses a fairly unusual scheme of lexical scoping that can be particularly useful in statistical work. This was mentioned briefly in one lecture but not fully explained.

Although I think this course could have been much better, I certainly did learn some things about R programming that I wanted to pick up, and overall I'm glad that I took the time to participate in the course.